


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       UpperCAmelCase_   (   unittest.TestCase       ):

   '''simple docstring'''




   @property
   def _snake_case       (						self					):






     """simple docstring"""





     torch.manual_seed(0					)
     lowerCamelCase					:   Union[str, Any]       =							UNetaDModel(
         block_out_channels=(32, 64)		,							layers_per_block=2		,							sample_size=32		,							in_channels=3		,							out_channels=3		,							down_block_types=("DownBlock2D", "AttnDownBlock2D")		,							up_block_types=("AttnUpBlock2D", "UpBlock2D")		,							)
     return model






   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   str       =							self.dummy_uncond_unet
     lowerCamelCase					:   Optional[int]       =							ScoreSdeVeScheduler()

     lowerCamelCase					:   List[str]       =							ScoreSdeVePipeline(unet=__A		,							scheduler=__A					)
     sde_ve.to(__A					)
     sde_ve.set_progress_bar_config(disable=__A					)

     lowerCamelCase					:   str       =							torch.manual_seed(0					)
     lowerCamelCase					:   Tuple       =							sde_ve(num_inference_steps=2		,							output_type="numpy"		,							generator=__A					).images

     lowerCamelCase					:   List[str]       =							torch.manual_seed(0					)
     lowerCamelCase					:   Dict       =							sde_ve(num_inference_steps=2		,							output_type="numpy"		,							generator=__A		,							return_dict=__A					)[
         0
     ]

     lowerCamelCase					:   Union[str, Any]       =							image[0, -3:, -3:, -1]
     lowerCamelCase					:   Optional[Any]       =							image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     lowerCamelCase					:   List[str]       =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2



@slow
@require_torch
class       UpperCAmelCase_   (   unittest.TestCase       ):

   '''simple docstring'''




   def _snake_case       (						self					):






     """simple docstring"""





     lowerCamelCase					:   List[str]       =							"google/ncsnpp-church-256"
     lowerCamelCase					:   List[str]       =							UNetaDModel.from_pretrained(__A					)

     lowerCamelCase					:   List[str]       =							ScoreSdeVeScheduler.from_pretrained(__A					)

     lowerCamelCase					:   List[Any]       =							ScoreSdeVePipeline(unet=__A		,							scheduler=__A					)
     sde_ve.to(__A					)
     sde_ve.set_progress_bar_config(disable=__A					)

     lowerCamelCase					:   Union[str, Any]       =							torch.manual_seed(0					)
     lowerCamelCase					:   Tuple       =							sde_ve(num_inference_steps=10		,							output_type="numpy"		,							generator=__A					).images

     lowerCamelCase					:   List[Any]       =							image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)

     lowerCamelCase					:   List[Any]       =							np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
